This Rmarkdown file assesses the output of CheckV, DeepVirFinder, Kaiju, VIBRANT, VirSorter, and VirSorter2 on multiple training sets of microbial DNA, primarily from NCBI. Created from fungal, viral, bacterial, archeael, protist, and plasmid DNA sequences
Please reach out to James Riddell (riddell.26@buckeyemail.osu.edu) or Bridget Hegarty (beh53@case.edu) regarding any issues, or open an issue on github.
library(ggplot2)
There were 50 or more warnings (use warnings() to see the first 50)
library(plyr)
library(reshape2)
library(viridis)
library(tidyr)
library(dplyr)
library(readr)
library(data.table)
library(pROC)
Import the file that combines the results from each of the tools from running “combining_tool_output.Rmd”:
viruses <- read_tsv("../IntermediaryFiles/viral_tools_combined.tsv")
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
.default = col_double(),
seqtype = col_character(),
contig = col_character(),
checkv_provirus = col_character(),
checkv_quality = col_character(),
method.x = col_character(),
Classified = col_character(),
IDs_all = col_character(),
Seq = col_character(),
Kaiju_Viral = col_character(),
Kingdom = col_character(),
type = col_character(),
vibrant_quality = col_character(),
method.y = col_character(),
vibrant_prophage = col_character(),
vs2type = col_character(),
max_score_group = col_character()
)
ℹ Use `spec()` for the full column specifications.
viruses$checkv_completeness[is.na(viruses$checkv_completeness)] <- 0
There were 50 or more warnings (use warnings() to see the first 50)
This section defines a viralness score “keep_score” based on the tool classifications. A final keep_score above 1 indicates we will keep that sequence and call it viral.
VIBRANT Quality == “High Quality Draft”: +1 Quality == “Medium Quality Draft”: +1 Quality == “Low Quality Draft” & provirus == TRUE: +0.5
Virsorter2 Viral >= 50: +0.5 Viral >= 0.95: +0.5
Virsorter category == 1,2,4,5: +1 category == 3,6: +0.5
DeepVirFinder: Score >= 0.7: +0.5 Score >= 0.9: +0.5
Kaiju: Kaiju_viral = “cellular organisms”: -1 Kaiju_viral = “Viruses”: +1
CheckV If %unknown >= 75: +0.5 Hallmark > 2: +1 viral_genes == 0 and host_genes >= 1: keep_score = 0 If 3*viral_genes <= host_genes: keep_score = 0 If length > 50,000 and hallmark == 0: keep_score = 0
This script produces visualizations of these combined viral scorings and includes ecological metrics like alpha diversity.
You can decide which combination is appropriate for them and only need use the tools appropriate for your data.
#seeing about a length cutoff
viruses <- viruses[viruses$checkv_length>=10000,]
There were 16 warnings (use warnings() to see them)
getting_viral_set_1 <- function(input_seqs,
include_vibrant=FALSE,
include_virsorter2=FALSE,
include_deepvirfinder=FALSE,
include_tuning=FALSE,
include_kaiju=FALSE,
include_virsorter=FALSE) {
keep_score <- rep(0, nrow(input_seqs))
if (include_vibrant) {
keep_score[input_seqs$vibrant_quality=="high quality draft"] <- keep_score[input_seqs$vibrant_quality=="high quality draft"] + 1
keep_score[input_seqs$vibrant_quality=="medium quality draft"] <- keep_score[input_seqs$vibrant_quality=="medium quality draft"] + 1
keep_score[input_seqs$vibrant_quality=="low quality draft" & input_seqs$checkv_provirus=="Yes"] <- keep_score[input_seqs$vibrant_quality=="low quality draft" & input_seqs$checkv_provirus=="Yes"] + 0.5
# keep_score[input_seqs$vibrant_quality=="low quality draft"] <- keep_score[input_seqs$vibrant_quality=="low quality draft"] + 0.5
}
if (include_virsorter2) {
keep_score[input_seqs$viral>=50] <- keep_score[input_seqs$viral>=50] + 0.5
keep_score[input_seqs$viral>=95] <- keep_score[input_seqs$viral>=95] + 0.5
}
if (include_virsorter) {
keep_score[input_seqs$category==1] <- keep_score[input_seqs$category==1] + 1
keep_score[input_seqs$category==2] <- keep_score[input_seqs$category==2] + 0.5
# keep_score[input_seqs$category==3] <- keep_score[input_seqs$category==3] + 0.5
keep_score[input_seqs$category==4] <- keep_score[input_seqs$category==4] + 1
keep_score[input_seqs$category==5] <- keep_score[input_seqs$category==5] + 0.5
# keep_score[input_seqs$category==6] <- keep_score[input_seqs$category==6] + 0.5
}
if (include_deepvirfinder) {
keep_score[input_seqs$score>=0.7 & input_seqs$checkv_length<20000] <- keep_score[input_seqs$score>=0.7 & input_seqs$checkv_length<20000] + 0.5
keep_score[input_seqs$score>=0.9 & input_seqs$checkv_length<20000] <- keep_score[input_seqs$score>=0.9 & input_seqs$checkv_length<20000] + 0.5
}
if (include_kaiju) {
keep_score[input_seqs$Kaiju_Viral=="cellular organisms"] <- keep_score[input_seqs$Kaiju_Viral=="cellular organisms"] - 1
keep_score[input_seqs$Kaiju_Viral=="Viruses"] <- keep_score[input_seqs$Kaiju_Viral=="Viruses"] + 0.5
}
if (include_tuning) {
keep_score[input_seqs$hallmark>2] <- keep_score[input_seqs$hallmark>2] + 1
keep_score[input_seqs$checkv_host_genes>50 & input_seqs$checkv_provirus=="No"] <- keep_score[input_seqs$checkv_host_genes>50 & input_seqs$checkv_provirus=="No"] - 1
keep_score[input_seqs$percent_unknown>=75 & input_seqs$checkv_length<50000] <- keep_score[input_seqs$percent_unknown>=75 & input_seqs$checkv_length<50000] + 0.5
keep_score[input_seqs$percent_viral>=50] <- keep_score[input_seqs$percent_viral>=50] + 0.5
#keep_score[input_seqs$hallmark>=(input_seqs$checkv_viral_genes/5)] <- keep_score[input_seqs$hallmark>=(input_seqs$checkv_viral_genes/5)] + 1 #add some ratio
keep_score[input_seqs$checkv_viral_genes==0 & input_seqs$checkv_host_genes>=1] <- keep_score[input_seqs$checkv_viral_genes==0 & input_seqs$checkv_host_genes>=1] - 1
keep_score[((input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes) & input_seqs$checkv_provirus=="No"] <- keep_score[((input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes) & input_seqs$checkv_provirus=="No"] - 1 # consider accounting for provirus designation
# keep_score[(input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes] <- 0 # consider accounting for provirus designation
keep_score[input_seqs$checkv_length>500000 & input_seqs$hallmark<=1] <- keep_score[input_seqs$checkv_length>500000 & input_seqs$hallmark<=1] - 1
keep_score[input_seqs$checkv_completeness<1 & input_seqs$hallmark<=1] <- keep_score[input_seqs$checkv_completeness>1 & input_seqs$hallmark<=1] - 1
}
return(keep_score)
}
Assessing performance against the “truth”
note that this is only as accurate as the annotations of the input sequences
this function calculates the precision, recall, and F1 score for each pipeline
assess_performance <- function(seqtype, keep_score) {
truepositive <- rep("not viral", length(seqtype))
truepositive[seqtype=="virus"] <- "viral"
#make confusion matrix
confusion_matrix <- rep("true negative", length(keep_score))
confusion_matrix[truepositive=="viral" & keep_score<=1] <- "false negative"
confusion_matrix[truepositive=="viral" & keep_score>=1] <- "true positive"
confusion_matrix[truepositive=="not viral" & keep_score>=1] <- "false positive"
TP <- table(confusion_matrix)[4]
FP <- table(confusion_matrix)[2]
TN <- table(confusion_matrix)[3]
FN <- table(confusion_matrix)[1]
precision <- TP/(TP+FP)
recall <- TP/(TP+FN)
F1 <- 2*precision*recall/(precision+recall)
MCC <- (TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN))
auc <- round(auc(truepositive, keep_score),4)
#by type metrics
fungal_FP <- table(confusion_matrix[seqtype=="fungi"])[2]
protist_FP <- table(confusion_matrix[seqtype=="protist"])[2]
bacterial_FP <- table(confusion_matrix[seqtype=="bacteria"])[2]
viral_FN <- table(confusion_matrix[seqtype=="virus"])[1]
performance <- c(precision, recall, F1, MCC, auc, fungal_FP,
protist_FP, bacterial_FP, viral_FN)
names(performance) <- c("precision", "recall", "F1", "MCC", "AUC", "fungal_FP",
"protist_FP", "bacterial_FP", "viral_FN")
return(performance)
}
combination of tools list
combos_list <- data.frame(toolcombo=rep(0, 64),
CheckV=rep(0, 64),
DVF=rep(0, 64),
Kaiju=rep(0, 64),
VIBRANT=rep(0, 64),
VS=rep(0, 64),
VS2=rep(0, 64))
p <- 1
for (i in c(0,1)){
for (j in c(0,1)){
for (k in c(0,1)){
for (l in c(0,1)){
for (m in c(0,1)){
for (n in c(0,1)){
combos_list$toolcombo[p] <- paste(i,j,k,l,m,n)
combos_list$toolcombo2[p] <- paste(if(i){"C"}else{"0"},if(j){"DVF"}else{"0"},
if(k){"K"}else{"0"},if(l){"VB"}else{"0"},
if(m){"VS"}else{"0"},if(n){"VS2"}else{"0"})
combos_list$CheckV[p] <- i
combos_list$DVF[p] <- j
combos_list$Kaiju[p] <- k
combos_list$VIBRANT[p] <- l
combos_list$VS[p] <- m
combos_list$VS2[p] <- n
p <- p+1
}
}
}
}
}
}
combos_list <- combos_list[-1,]
this function builds a list of all of the combinations that the user wants to test. In this case, we’re comparing the performance of all unique combinations of the six tools.
build_score_list <- function(input_seqs, combos) {
Warning messages:
1: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
2: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
3: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
4: Unknown or uninitialised column: `keep_score_visualize`.
5: Unknown or uninitialised column: `truepositive`.
output <- data.frame(precision=rep(0, nrow(combos)),
recall=rep(0, nrow(combos)),
F1=rep(0, nrow(combos)),
MCC=rep(0, nrow(combos)),
AUC=rep(0, nrow(combos)),
fungal_FP=rep(0, nrow(combos)),
protist_FP=rep(0, nrow(combos)),
bacterial_FP=rep(0, nrow(combos)),
viral_FN=rep(0, nrow(combos)))
for (i in 1:nrow(combos)) {
keep_score <- getting_viral_set_1(input_seqs, include_vibrant = combos$VIBRANT[i],
include_virsorter = combos$VS[i],
include_virsorter2 = combos$VS2[i],
include_tuning = combos$CheckV[i],
include_kaiju = combos$Kaiju[i],
include_deepvirfinder = combos$DVF[i])
output[i,1:9] <- assess_performance(input_seqs$seqtype, keep_score)
output$toolcombo[i] <- paste(combos$CheckV[i],combos$DVF[i],
combos$Kaiju[i], combos$VIBRANT[i],
combos$VS[i], combos$VS2[i])
}
output[is.na(output)] <- 0
return (output)
}
Calculate the performance of each pipeline
accuracy_scores <- data.frame(testing_set_index=rep(0, nrow(combos_list)*10),
precision=rep(0, nrow(combos_list)*10),
recall=rep(0, nrow(combos_list)*10),
F1=rep(0, nrow(combos_list)*10),
MCC=rep(0, nrow(combos_list)*10),
AUC=rep(0, nrow(combos_list)*10),
fungal_FP=rep(0, nrow(combos_list)*10),
protist_FP=rep(0, nrow(combos_list)*10),
bacterial_FP=rep(0, nrow(combos_list)*10),
viral_FN=rep(0, nrow(combos_list)*10))
accuracy_scores <- cbind(testing_set_index=rep(1, nrow(combos_list)),
build_score_list(viruses[viruses$Index==1,], combos_list))
for (i in 2:10) {
accuracy_scores <- rbind(accuracy_scores,
cbind(testing_set_index=rep(i, nrow(combos_list)),
build_score_list(viruses[viruses$Index==i,], combos_list)))
}
library("stringr")
accuracy_scores$numtools <- str_count(accuracy_scores$toolcombo, "1")
There were 11 warnings (use warnings() to see them)
#accuracy_scores <- accuracy_scores[order(accuracy_scores$numtools, decreasing=F),]
accuracy_scores <- accuracy_scores[order(accuracy_scores$MCC, decreasing=F),]
accuracy_scores$toolcombo <- factor(accuracy_scores$toolcombo, levels = unique(accuracy_scores$toolcombo))
accuracy_scores$numtools <- as.factor(accuracy_scores$numtools)
Visualize how the precision, recall, and F1 scores change across pipelines.
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
p2 <- ggplot(accuracy_scores, aes(x=toolcombo, y=F1,
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("F1 Score")
p2

ggplot(accuracy_scores, aes(x=toolcombo, y=precision,
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("Precision")

ggplot(accuracy_scores, aes(x=toolcombo, y=recall,
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("Recall")

ggplot(accuracy_scores, aes(x=precision, y=recall,
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Precision") +
ylab("Recall")

ggplot(accuracy_scores, aes(x=toolcombo, y=abs(precision-recall),
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("Precision-Recall")

ggplot(accuracy_scores, aes(x=toolcombo, y=MCC,
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("MCC")

ggplot(accuracy_scores, aes(x=toolcombo, y=AUC,
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("AUC")

ggplot(accuracy_scores, aes(x=toolcombo, y=fungal_FP,
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("Fungal False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=protist_FP,
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("Protist False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=bacterial_FP,
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("Bacterial False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=viral_FN,
color=numtools, fill=numtools)) +
geom_point(alpha=0.5) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("Viral False Negatives")

write_tsv(accuracy_scores, "20220927_accuracy_scores.tsv")
to do: add in clustering and ordination like in the drinking water R notebook
Experimenting
high precision example
viruses$keep_score_high_precision <- getting_viral_set_1(viruses, include_deepvirfinder = F,
There were 44 warnings (use warnings() to see them)
include_vibrant = T,
include_virsorter2 = F,
include_kaiju = T,
include_tuning = T,
include_virsorter = F)
number of items to replace is not a multiple of replacement length
viruses$confusion_matrix_high_precision <- "true negative"
There were 11 warnings (use warnings() to see them)
viruses$confusion_matrix_high_precision[viruses$seqtype=="virus" & viruses$keep_score_high_precision<1] <- "false negative"
viruses$confusion_matrix_high_precision[viruses$seqtype=="virus" & viruses$keep_score_high_precision>=1] <- "true positive"
viruses$confusion_matrix_high_precision[viruses$seqtype!="virus" & viruses$keep_score_high_precision>=1] <- "false positive"
visualizing confusion matrix by taxa
confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_precision, viruses$seqtype, viruses$Index))
The melt generic in data.table has been passed a table and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(table(viruses$confusion_matrix_high_precision, viruses$seqtype, viruses$Index)). In the next version, this warning will become an error.
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
Warning messages:
1: Unknown or uninitialised column: `confusion_matrix_high_recall`.
2: Unknown or uninitialised column: `confusion_matrix_high_recall`.
3: Unknown or uninitialised column: `confusion_matrix_high_recall`.
4: Unknown or uninitialised column: `keep_score_visualize`.
5: Unknown or uninitialised column: `truepositive`.
ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
fill=confusion_matrix,
color=confusion_matrix)) +
geom_bar(stat="identity") +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("Number of Sequences") +
ylab("") +
facet_wrap(~seqtype, scales = "free") +
coord_flip()

ggplot(viruses, aes(x=checkv_viral_genes, y=confusion_matrix_high_precision,
Warning messages:
1: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
2: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
3: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
4: Unknown or uninitialised column: `keep_score_visualize`.
5: Unknown or uninitialised column: `truepositive`.
6: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
7: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
8: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
9: Unknown or uninitialised column: `keep_score_visualize`.
10: Unknown or uninitialised column: `truepositive`.
fill=confusion_matrix_high_precision,
color=confusion_matrix_high_precision)) +
geom_boxplot(alpha=0.3) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("Number of Viral Sequences") +
ylab("") +
facet_wrap(~seqtype, scales = "free") +
coord_flip()

ggplot(viruses, aes(x=percent_viral, y=confusion_matrix_high_precision,
fill=confusion_matrix_high_precision,
color=confusion_matrix_high_precision)) +
geom_boxplot(alpha=0.3) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("Percent Genes Viral") +
ylab("") +
facet_wrap(~seqtype, scales = "free") +
coord_flip()

ggplot(viruses, aes(x=hallmark, y=confusion_matrix_high_precision,
fill=confusion_matrix_high_precision,
color=confusion_matrix_high_precision)) +
geom_boxplot(alpha=0.3) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("Number of Hallmark Genes") +
ylab("") +
facet_wrap(~seqtype, scales = "free") +
coord_flip()

ggplot(viruses, aes(x=hallmark, y=checkv_viral_genes,
fill=confusion_matrix_high_precision,
color=confusion_matrix_high_precision)) +
geom_point(alpha=0.3) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("Number of Hallmark Genes") +
ylab("Number of Viral Genes") +
facet_wrap(~seqtype, scales = "free") +
coord_flip()

viruses_false_positive <- viruses[viruses$confusion_matrix_high_precision=="false positive",]
Warning messages:
1: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
2: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
3: Unknown or uninitialised column: `confusion_matrix_high_MCC`.
4: Unknown or uninitialised column: `keep_score_visualize`.
5: Unknown or uninitialised column: `truepositive`.
viruses_false_negative <- viruses[viruses$confusion_matrix_high_precision=="false negative",]
ggplot(viruses, aes(x=hallmark, y=checkv_viral_genes,
fill=checkv_length,
color=checkv_length,
shape=checkv_provirus)) +
geom_point(alpha=0.3) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Number of Hallmark Genes") +
ylab("Number of Viral Genes") +
facet_wrap(~seqtype, scales = "free") +
coord_flip()

ggplot(viruses_false_positive, aes(x=hallmark, y=checkv_length,
fill=checkv_viral_genes,
color=checkv_viral_genes,
shape=checkv_provirus)) +
geom_point(alpha=0.3) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Number of Hallmark Genes") +
ylab("Contig Length") +
facet_wrap(~seqtype, scales = "free") +
coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="bacteria"], aes(x=hallmark, y=checkv_length,
fill=checkv_viral_genes,
color=checkv_viral_genes,
shape=checkv_provirus)) +
geom_point(alpha=0.3) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Number of Hallmark Genes") +
ylab("Contig Length") +
facet_wrap(~Kaiju_Viral, scales = "free") +
coord_flip()
Error:
! Must subset columns with a valid subscript vector.
ℹ Logical subscripts must match the size of the indexed input.
✖ Input has size 43 but subscript `viruses_false_positive$seqtype == "bacteria"` has size 1670.
Backtrace:
1. ggplot2::ggplot(...)
3. tibble:::`[.tbl_df`(...)
4. tibble:::vectbl_as_col_location(...)
7. vctrs::vec_as_location(j, n, names)
8. vctrs (local) `<fn>`()
9. vctrs:::stop_indicator_size(...)
table(viruses$hallmark[viruses$confusion_matrix_high_precision=="false positive"]>0)
FALSE TRUE
4398 2608
table(viruses$percent_host[viruses$confusion_matrix_high_precision=="false positive"]<50)
FALSE TRUE
855 6151
high MCC example
viruses$keep_score_high_MCC <- getting_viral_set_1(viruses, include_deepvirfinder = F,
There were 40 warnings (use warnings() to see them)
include_vibrant = T,
include_virsorter2 = T,
include_kaiju = T,
include_tuning = T,
include_virsorter = T)
number of items to replace is not a multiple of replacement length
viruses$confusion_matrix_high_MCC <- "true negative"
Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`.
2: Unknown or uninitialised column: `truepositive`.
viruses$confusion_matrix_high_MCC[viruses$seqtype=="virus" & viruses$keep_score_high_MCC<1] <- "false negative"
viruses$confusion_matrix_high_MCC[viruses$seqtype=="virus" & viruses$keep_score_high_MCC>=1] <- "true positive"
viruses$confusion_matrix_high_MCC[viruses$seqtype!="virus" & viruses$keep_score_high_MCC>=1] <- "false positive"
visualizing confusion matrix by taxa
confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_MCC, viruses$seqtype, viruses$Index))
The melt generic in data.table has been passed a table and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(table(viruses$confusion_matrix_high_MCC, viruses$seqtype, viruses$Index)). In the next version, this warning will become an error.
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`.
2: Unknown or uninitialised column: `truepositive`.
fill=confusion_matrix,
color=confusion_matrix)) +
geom_bar(stat="identity") +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("Number of Sequences") +
ylab("") +
facet_wrap(~seqtype, scales = "free") +
coord_flip()

ggplot(viruses, aes(x=checkv_length, y=keep_score_high_MCC,
Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`.
2: Unknown or uninitialised column: `truepositive`.
fill=confusion_matrix_high_MCC,
color=confusion_matrix_high_MCC)) +
geom_point(stat="identity", shape=21) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("Sequence Length (bp)") +
ylab("Pipeline Viral Score") +
facet_wrap(~seqtype) +
scale_x_log10()

high recall example
viruses$keep_score_high_recall <- getting_viral_set_1(viruses, include_deepvirfinder = T,
Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`.
2: Unknown or uninitialised column: `truepositive`.
include_vibrant = T,
include_virsorter2 = T,
include_kaiju = T,
include_tuning = T,
include_virsorter = T)
number of items to replace is not a multiple of replacement length
viruses$confusion_matrix_high_recall <- "true negative"
viruses$confusion_matrix_high_recall[viruses$seqtype=="virus" & viruses$keep_score_high_recall<1] <- "false negative"
viruses$confusion_matrix_high_recall[viruses$seqtype=="virus" & viruses$keep_score_high_recall>=1] <- "true positive"
viruses$confusion_matrix_high_recall[viruses$seqtype!="virus" & viruses$keep_score_high_recall>=1] <- "false positive"
accuracy:
length(grep("true", viruses$confusion_matrix_high_recall))/nrow(viruses)
[1] 0.9247793
visualizing confusion matrix by taxa
confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_recall, viruses$seqtype, viruses$Index))
The melt generic in data.table has been passed a table and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(table(viruses$confusion_matrix_high_recall, viruses$seqtype, viruses$Index)). In the next version, this warning will become an error.
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`.
2: Unknown or uninitialised column: `truepositive`.
p2 <- ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
fill=confusion_matrix,
color=confusion_matrix)) +
geom_bar(stat="identity") +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("Number of Sequences") +
ylab("") +
facet_wrap(~seqtype, scales = "free") +
coord_flip()
p2

ggplot(viruses, aes(x=checkv_completeness, y=hallmark,
Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`.
2: Unknown or uninitialised column: `truepositive`.
fill=confusion_matrix_high_recall,
color=confusion_matrix_high_recall)) +
geom_point(stat="identity", shape=21) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("CheckV Completeness") +
ylab("Number of Hallmark Genes") +
facet_wrap(~seqtype) +
scale_x_log10()

ggplot(viruses, aes(x=checkv_completeness, y=keep_score_high_MCC,
Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`.
2: Unknown or uninitialised column: `truepositive`.
3: Unknown or uninitialised column: `keep_score_visualize`.
4: Unknown or uninitialised column: `truepositive`.
fill=confusion_matrix_high_recall,
color=confusion_matrix_high_recall)) +
geom_point(stat="identity", shape=21) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("CheckV Completeness") +
ylab("Pipeline Viral Score") +
facet_wrap(~seqtype) +
scale_x_log10()

ggplot(viruses, aes(x=confusion_matrix_high_recall, y=checkv_length,
There were 12 warnings (use warnings() to see them)
fill=confusion_matrix_high_recall,
color=confusion_matrix_high_recall)) +
geom_boxplot() +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(4)), 0.5),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
scale_color_manual(name="",
values = alpha(rev(pal(4)), 1),
labels=c("false negative", "false positive",
"true negative", "true positive")) +
xlab("Sequence Length (bp)") +
ylab("Pipeline Viral Score") +
scale_y_log10()

looking at false negatives
viruses_false_negs <- viruses[(viruses$seqtype=="virus" & viruses$keep_score_high_recall<1),]
looking at protists calling viral
viruses_false_pos_protists <- viruses[(viruses$seqtype=="protist" & viruses$keep_score_high_recall>=1),]
There were 40 warnings (use warnings() to see them)
ROC
library(pROC)
viruses$truepositive <- rep(0, nrow(viruses))
viruses$truepositive[viruses$seqtype=="virus"] <- 1
rocobj <- roc(viruses$truepositive, viruses$keep_score)
rocobj_all <- roc(viruses$truepositive, viruses$keep_score_all)
auc <- round(auc(viruses$truepositive, viruses$keep_score),4)
auc_all <- round(auc(viruses$truepositive, viruses$keep_score_all),4)
#create ROC plot
ggroc(rocobj, colour = 'steelblue', size = 2) +
ggtitle(paste0('ROC Curve ', '(AUC = ', auc, ')')) +
coord_equal()
ggroc(rocobj_all, colour = 'green', size = 2) +
ggtitle(paste0('ROC Curve ', '(AUC = ', auc_all, ')'))
Sensitivity: The probability that the model predicts a positive outcome for an observation when indeed the outcome is positive. Specificity: The probability that the model predicts a negative outcome for an observation when indeed the outcome is negative.
Comparing behavior of all testing sets combined (clustering analyses)
viral_scores <- matrix(data=0, nrow=nrow(viruses), ncol=nrow(combos_list))
num_viruses <- data.frame(toolcombo=rep(0, nrow(combos_list)),
num_viruses=rep(0, nrow(combos_list)))
for (i in 1:nrow(combos_list)) {
viral_scores[,i] <- getting_viral_set_1(viruses, include_vibrant = combos_list$VIBRANT[i],
include_virsorter = combos_list$VS[i],
include_virsorter2 = combos_list$VS2[i],
include_tuning = combos_list$CheckV[i],
include_kaiju = combos_list$Kaiju[i],
include_deepvirfinder = combos_list$DVF[i])
num_viruses$num_viruses[i] <- table(viral_scores[,i]>=1)[[2]]
num_viruses$toolcombo[i] <- combos_list$toolcombo[i]
num_viruses$toolcombo2[i] <- combos_list$toolcombo2[i]
}
num_viruses$numtools <- str_count(num_viruses$toolcombo, "1")
num_viruses <- num_viruses[order(num_viruses$num_viruses, decreasing=F),]
num_viruses$toolcombo <- factor(num_viruses$toolcombo, levels = unique(num_viruses$toolcombo))
num_viruses$toolcombo2 <- factor(num_viruses$toolcombo2, levels = unique(num_viruses$toolcombo2))
num_viruses$numtools <- as.factor(num_viruses$numtools)
ggplot(num_viruses, aes(x=toolcombo, y=num_viruses,
color=numtools, fill=numtools)) +
geom_point() +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("Num Viruses Predicted")
ggplot(num_viruses, aes(x=toolcombo2, y=num_viruses,
color=numtools, fill=numtools)) +
geom_point() +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
ylab("Num Viruses Predicted")
ggplot(num_viruses, aes(x=numtools, y=num_viruses)) +
geom_boxplot(aes(color=numtools)) +
geom_point(aes(color=numtools, fill=numtools)) +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14, angle = 90),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
xlab("Number of Tools") +
ylab("Num Viruses Predicted")
viral_scores_nozeros <- viral_scores[rowSums(viral_scores)>0,]
viral_scores_nozeros <- viral_scores_nozeros + 1
viral_scores_nozeros <- as.data.frame(viral_scores_nozeros)
colnames(viral_scores_nozeros) <- num_viruses$toolcombo2
tooldata <- num_viruses
rownames(tooldata) <- tooldata$toolcombo2
tooldata <- num_viruses
rownames(tooldata) <- tooldata$toolcombo2
physeq_pooled <- phyloseq(otu_table(viral_scores_nozeros, taxa_are_rows = T),
sample_data(tooldata))
ordination <- phyloseq::ordinate(physeq =physeq_pooled, method = "PCoA", distance = "bray")
phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
shape="numtools", color="num_viruses") +
geom_point(size = 3) +
theme_bw() +
geom_label(label=tooldata$toolcombo)
phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
shape="numtools", color="num_viruses") +
geom_point(size = 3) +
theme_bw()
to do: try coloring above based on the F1 scores of the testing set on each combination
bray_dist <- phyloseq::distance(physeq_pooled, method="bray")
clusters <- hclust(dist(bray_dist))
plot(clusters)
myclusters <- cutree(clusters, h=1.1)
names(myclusters[myclusters==1])
names(myclusters[myclusters==2])
names(myclusters[myclusters==3])
names(myclusters[myclusters==4])
names(myclusters[myclusters==5])
myclusters_df <- tibble(combo=names(myclusters),
cluster_index=myclusters)
myclusters_df <- separate(myclusters_df, col=combo, into=c("CheckV", "DVF",
"Kaiju", "VIBRANT",
"VirSorter", "VirSorter2"),
sep=" ", remove = F)
tool_count <- as.data.frame(rbind(table(myclusters_df$CheckV, myclusters_df$cluster_index)[2,],
table(myclusters_df$DVF, myclusters_df$cluster_index)[2,],
table(myclusters_df$Kaiju, myclusters_df$cluster_index)[2,],
table(myclusters_df$VIBRANT, myclusters_df$cluster_index)[2,],
table(myclusters_df$VirSorter, myclusters_df$cluster_index)[2,],
table(myclusters_df$VirSorter2, myclusters_df$cluster_index)[2,])
)
tool_count$method <- c("CheckV", "DVF", "Kaiju", "VIBRANT", "VirSorter", "VirSorter2")
tool_count <- melt(tool_count)
colnames(tool_count) <- c("tool", "cluster_index", "tool_count")
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
ggplot(tool_count, aes(x=cluster_index, y=tool_count,
fill=cluster_index,
color=cluster_index)) +
geom_bar(stat="identity") +
theme_light() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "bottom",
axis.text.y=element_text(size=14),
axis.text.x=element_text(size=14),
legend.text=element_text(size=12),
axis.title=element_text(size=16),
) +
scale_fill_manual(name="",
values = alpha(rev(pal(6)), 0.5)) +
scale_color_manual(name="",
values = alpha(rev(pal(6)), 1)) +
xlab("Cluster") +
ylab("Number of Times in Cluster") +
facet_wrap(~tool, scales = "free")
---
title: "Viral Sequence Sorting Tools Evaluation"
author: Bridget Hegarty, James Riddell
date: 07-22-2022
output: html_notebook
---
This Rmarkdown file assesses the output of CheckV, DeepVirFinder, Kaiju,
VIBRANT, VirSorter, and VirSorter2 on multiple training sets of microbial DNA, 
primarily from NCBI. Created from fungal, viral, bacterial, archeael, protist,
and plasmid DNA sequences

Please reach out to James Riddell (riddell.26@buckeyemail.osu.edu) or
Bridget Hegarty (beh53@case.edu) regarding any issues, or open an issue on github.

```{r setup-library}
library(ggplot2)
library(plyr)
library(reshape2)
library(viridis)
library(tidyr)
library(dplyr)
library(readr)
library(data.table)
library(pROC)
```

Import the file that combines the results from each of the tools from running "combining_tool_output.Rmd":
```{r}
viruses <- read_tsv("../IntermediaryFiles/viral_tools_combined.tsv")
```

```{r}
viruses$checkv_completeness[is.na(viruses$checkv_completeness)] <- 0
```


This section defines a viralness score "keep_score" based on the tool classifications. 
A final keep_score above 1 indicates we will keep that sequence and call it viral.

VIBRANT
    Quality == "High Quality Draft": +1
    Quality == "Medium Quality Draft": +1
    Quality == "Low Quality Draft" & provirus == TRUE: +0.5

Virsorter2
    Viral >= 50: +0.5
    Viral >= 0.95: +0.5

Virsorter
    category ==  1,2,4,5: +1
    category == 3,6: +0.5

DeepVirFinder:
    Score >= 0.7: +0.5
    Score >= 0.9: +0.5

Kaiju:
    Kaiju_viral = "cellular organisms": -1
    Kaiju_viral = "Viruses": +1

CheckV
    If %unknown >= 75: +0.5
    Hallmark > 2: +1
    viral_genes == 0 and host_genes >= 1: keep_score = 0
    If 3*viral_genes <= host_genes: keep_score = 0
    If length > 50,000 and hallmark == 0: keep_score = 0
    

This script produces visualizations of these combined viral scorings and
includes ecological metrics like alpha diversity.

You can decide which combination is appropriate for them and only need use the
tools appropriate for your data.

#seeing about a length cutoff
```{r}
viruses <- viruses[viruses$checkv_length>=5000,]
```


```{r getting_viral_set_1}
getting_viral_set_1 <- function(input_seqs,
                                include_vibrant=FALSE, 
                                include_virsorter2=FALSE,
                                include_deepvirfinder=FALSE,
                                include_tuning=FALSE,
                                include_kaiju=FALSE,
                                include_virsorter=FALSE) {
  
  keep_score <- rep(0, nrow(input_seqs))
  
  if (include_vibrant) {
    keep_score[input_seqs$vibrant_quality=="high quality draft"] <- keep_score[input_seqs$vibrant_quality=="high quality draft"] + 1
    keep_score[input_seqs$vibrant_quality=="medium quality draft"] <- keep_score[input_seqs$vibrant_quality=="medium quality draft"] + 1
    keep_score[input_seqs$vibrant_quality=="low quality draft" & input_seqs$checkv_provirus=="Yes"] <- keep_score[input_seqs$vibrant_quality=="low quality draft" & input_seqs$checkv_provirus=="Yes"] + 0.5
#    keep_score[input_seqs$vibrant_quality=="low quality draft"] <- keep_score[input_seqs$vibrant_quality=="low quality draft"] + 0.5
  }
  
  if (include_virsorter2) {
    keep_score[input_seqs$viral>=50] <- keep_score[input_seqs$viral>=50] + 0.5
    keep_score[input_seqs$viral>=95] <- keep_score[input_seqs$viral>=95] + 0.5
  }
  
  if (include_virsorter) {
    keep_score[input_seqs$category==1] <- keep_score[input_seqs$category==1] + 1
    keep_score[input_seqs$category==2] <- keep_score[input_seqs$category==2] + 0.5
#    keep_score[input_seqs$category==3] <- keep_score[input_seqs$category==3] + 0.5
    keep_score[input_seqs$category==4] <- keep_score[input_seqs$category==4] + 1
    keep_score[input_seqs$category==5] <- keep_score[input_seqs$category==5] + 0.5
#    keep_score[input_seqs$category==6] <- keep_score[input_seqs$category==6] + 0.5
  }
  
  if (include_deepvirfinder) {
    keep_score[input_seqs$score>=0.7 & input_seqs$checkv_length<20000] <- keep_score[input_seqs$score>=0.7 & input_seqs$checkv_length<20000] + 0.5
   keep_score[input_seqs$score>=0.9 & input_seqs$checkv_length<20000] <- keep_score[input_seqs$score>=0.9 & input_seqs$checkv_length<20000] + 0.5
  }
  
  if (include_kaiju) {
    keep_score[input_seqs$Kaiju_Viral=="cellular organisms"] <- keep_score[input_seqs$Kaiju_Viral=="cellular organisms"] - 1
    keep_score[input_seqs$Kaiju_Viral=="Viruses"] <- keep_score[input_seqs$Kaiju_Viral=="Viruses"] + 0.5
  }
  
  if (include_tuning) {
    keep_score[input_seqs$hallmark>2] <- keep_score[input_seqs$hallmark>2] + 1
    keep_score[input_seqs$checkv_host_genes>50 & input_seqs$checkv_provirus=="No"] <- keep_score[input_seqs$checkv_host_genes>50 & input_seqs$checkv_provirus=="No"] - 1
    keep_score[input_seqs$percent_unknown>=75 & input_seqs$checkv_length<50000] <- keep_score[input_seqs$percent_unknown>=75 & input_seqs$checkv_length<50000] + 0.5
    keep_score[input_seqs$percent_viral>=50] <- keep_score[input_seqs$percent_viral>=50] + 0.5
    #keep_score[input_seqs$hallmark>=(input_seqs$checkv_viral_genes/5)] <- keep_score[input_seqs$hallmark>=(input_seqs$checkv_viral_genes/5)] + 1 #add some ratio
    keep_score[input_seqs$checkv_viral_genes==0 & input_seqs$checkv_host_genes>=1] <- keep_score[input_seqs$checkv_viral_genes==0 & input_seqs$checkv_host_genes>=1] - 1
    keep_score[((input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes) & input_seqs$checkv_provirus=="No"] <- keep_score[((input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes) & input_seqs$checkv_provirus=="No"] - 1 # consider accounting for provirus designation
#    keep_score[(input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes] <- 0 # consider accounting for provirus designation
    keep_score[input_seqs$checkv_length>500000 & input_seqs$hallmark<=1] <- keep_score[input_seqs$checkv_length>500000 & input_seqs$hallmark<=1] - 1
    keep_score[input_seqs$checkv_completeness<1 & input_seqs$hallmark<=1] <- keep_score[input_seqs$checkv_completeness>1 & input_seqs$hallmark<=1] - 1
  }
  
  return(keep_score)
  
}
```



# Assessing performance against the "truth"
note that this is only as accurate as the annotations of the input sequences

this function calculates the precision, recall, and F1 score for each pipeline
```{r}
assess_performance <- function(seqtype, keep_score) {
  
  truepositive <- rep("not viral", length(seqtype))
  truepositive[seqtype=="virus"] <- "viral"
  
  #make confusion matrix
  confusion_matrix <- rep("true negative", length(keep_score))
  confusion_matrix[truepositive=="viral" & keep_score<=1] <- "false negative"
  confusion_matrix[truepositive=="viral" & keep_score>=1] <- "true positive"
  confusion_matrix[truepositive=="not viral" & keep_score>=1] <- "false positive"
  
  TP <- table(confusion_matrix)[4]
  FP <- table(confusion_matrix)[2]
  TN <- table(confusion_matrix)[3]
  FN <- table(confusion_matrix)[1]
  
  precision <- TP/(TP+FP)
  recall <- TP/(TP+FN)
  F1 <- 2*precision*recall/(precision+recall)
  
  MCC <- (TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN))
  
  auc <- round(auc(truepositive, keep_score),4)
  
  #by type metrics
  fungal_FP <- table(confusion_matrix[seqtype=="fungi"])[2]
  protist_FP <- table(confusion_matrix[seqtype=="protist"])[2]
  bacterial_FP <- table(confusion_matrix[seqtype=="bacteria"])[2]
  viral_FN <- table(confusion_matrix[seqtype=="virus"])[1]
  
  performance <- c(precision, recall, F1, MCC, auc, fungal_FP, 
                   protist_FP, bacterial_FP, viral_FN)
  names(performance) <- c("precision", "recall", "F1", "MCC", "AUC", "fungal_FP",
                          "protist_FP", "bacterial_FP", "viral_FN")
  
  return(performance)
}
```

combination of tools list
```{r}
combos_list <- data.frame(toolcombo=rep(0, 64),
                          CheckV=rep(0, 64),
                          DVF=rep(0, 64),
                          Kaiju=rep(0, 64),
                          VIBRANT=rep(0, 64),
                          VS=rep(0, 64),
                          VS2=rep(0, 64))
p <- 1

for (i in c(0,1)){
  for (j in c(0,1)){
    for (k in c(0,1)){
      for (l in c(0,1)){
        for (m in c(0,1)){
          for (n in c(0,1)){
            combos_list$toolcombo[p] <- paste(i,j,k,l,m,n)
            combos_list$toolcombo2[p] <- paste(if(i){"C"}else{"0"},if(j){"DVF"}else{"0"},
                                               if(k){"K"}else{"0"},if(l){"VB"}else{"0"},
                                               if(m){"VS"}else{"0"},if(n){"VS2"}else{"0"})
            combos_list$CheckV[p] <- i
            combos_list$DVF[p] <- j
            combos_list$Kaiju[p] <- k
            combos_list$VIBRANT[p] <- l
            combos_list$VS[p] <- m
            combos_list$VS2[p] <- n
            p <- p+1
          }
        }
      }
    }
  }
}

combos_list <- combos_list[-1,]
```

this function builds a list of all of the combinations that the user wants to 
test. 
In this case, we're comparing the performance of all unique combinations of the 
six tools.
```{r}
build_score_list <- function(input_seqs, combos) {
  output <- data.frame(precision=rep(0, nrow(combos)),
                       recall=rep(0, nrow(combos)),
                       F1=rep(0, nrow(combos)),
                       MCC=rep(0, nrow(combos)),
                       AUC=rep(0, nrow(combos)),
                       fungal_FP=rep(0, nrow(combos)),
                       protist_FP=rep(0, nrow(combos)),
                       bacterial_FP=rep(0, nrow(combos)),
                       viral_FN=rep(0, nrow(combos)))
  for (i in 1:nrow(combos)) {
    keep_score <- getting_viral_set_1(input_seqs, include_vibrant = combos$VIBRANT[i],
                                            include_virsorter = combos$VS[i],
                                            include_virsorter2 = combos$VS2[i],
                                            include_tuning = combos$CheckV[i],
                                            include_kaiju = combos$Kaiju[i],
                                            include_deepvirfinder = combos$DVF[i])
  
    output[i,1:9] <- assess_performance(input_seqs$seqtype, keep_score)
    
    output$toolcombo[i] <- paste(combos$CheckV[i],combos$DVF[i],
                                 combos$Kaiju[i], combos$VIBRANT[i],
                                 combos$VS[i], combos$VS2[i])
  }
  
  output[is.na(output)] <- 0

  return (output)
}
```

## Calculate the performance of each pipeline
```{r}
accuracy_scores <- data.frame(testing_set_index=rep(0, nrow(combos_list)*10),
                      precision=rep(0, nrow(combos_list)*10),
                       recall=rep(0, nrow(combos_list)*10),
                       F1=rep(0, nrow(combos_list)*10),
                       MCC=rep(0, nrow(combos_list)*10), 
                      AUC=rep(0, nrow(combos_list)*10),
                      fungal_FP=rep(0, nrow(combos_list)*10),
                      protist_FP=rep(0, nrow(combos_list)*10),
                      bacterial_FP=rep(0, nrow(combos_list)*10),
                      viral_FN=rep(0, nrow(combos_list)*10))

accuracy_scores <- cbind(testing_set_index=rep(1, nrow(combos_list)),
                              build_score_list(viruses[viruses$Index==1,], combos_list))
for (i in 2:10) {
  accuracy_scores <- rbind(accuracy_scores,
                           cbind(testing_set_index=rep(i, nrow(combos_list)),
                              build_score_list(viruses[viruses$Index==i,], combos_list)))
}
```

```{r}
library("stringr")
```

```{r}
accuracy_scores$numtools <- str_count(accuracy_scores$toolcombo, "1")
#accuracy_scores <- accuracy_scores[order(accuracy_scores$numtools, decreasing=F),]
accuracy_scores <- accuracy_scores[order(accuracy_scores$MCC, decreasing=F),]
accuracy_scores$toolcombo <- factor(accuracy_scores$toolcombo, levels = unique(accuracy_scores$toolcombo))
accuracy_scores$numtools <- as.factor(accuracy_scores$numtools)
```


## Visualize how the precision, recall, and F1 scores change across pipelines.
```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
p2 <- ggplot(accuracy_scores, aes(x=toolcombo, y=F1, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("F1 Score")
p2
ggplot(accuracy_scores, aes(x=toolcombo, y=precision, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Precision")
ggplot(accuracy_scores, aes(x=toolcombo, y=recall, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Recall")
ggplot(accuracy_scores, aes(x=precision, y=recall, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Precision") +
  ylab("Recall")
ggplot(accuracy_scores, aes(x=toolcombo, y=abs(precision-recall), 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Precision-Recall")
ggplot(accuracy_scores, aes(x=toolcombo, y=MCC, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("MCC")
ggplot(accuracy_scores, aes(x=toolcombo, y=AUC, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("AUC")
ggplot(accuracy_scores, aes(x=toolcombo, y=fungal_FP, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Fungal False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=protist_FP, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Protist False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=bacterial_FP, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Bacterial False Positives")
ggplot(accuracy_scores, aes(x=toolcombo, y=viral_FN, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Viral False Negatives")
```

```{r}
write_tsv(accuracy_scores, "20220927_accuracy_scores.tsv")
```

to do: add in clustering and ordination like in the drinking water R notebook

# Experimenting

## high precision example
```{r}
viruses$keep_score_high_precision <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_kaiju = T,
                                              include_tuning = T,
                                              include_virsorter = F)
```


```{r}
viruses$confusion_matrix_high_precision <- "true negative"
viruses$confusion_matrix_high_precision[viruses$seqtype=="virus" & viruses$keep_score_high_precision<1] <- "false negative"
viruses$confusion_matrix_high_precision[viruses$seqtype=="virus" & viruses$keep_score_high_precision>=1] <- "true positive"
viruses$confusion_matrix_high_precision[viruses$seqtype!="virus" & viruses$keep_score_high_precision>=1] <- "false positive"
```

visualizing confusion matrix by taxa
```{r}
confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_precision, viruses$seqtype, viruses$Index))
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
```



```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
```

```{r}
ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
```

```{r}
 ggplot(viruses, aes(x=checkv_viral_genes, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Viral Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

 ggplot(viruses, aes(x=percent_viral, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Percent Genes Viral") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

 ggplot(viruses, aes(x=hallmark, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Hallmark Genes") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
 
ggplot(viruses, aes(x=hallmark, y=checkv_viral_genes,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Hallmark Genes") +
  ylab("Number of Viral Genes") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
```

```{r}
viruses_false_positive <- viruses[viruses$confusion_matrix_high_precision=="false positive",]
viruses_false_negative <- viruses[viruses$confusion_matrix_high_precision=="false negative",]
```

```{r}
ggplot(viruses, aes(x=hallmark, y=checkv_viral_genes,
                   fill=checkv_length,
                   color=checkv_length,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Number of Viral Genes") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive, aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="bacteria"], aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="fungi"], aes(x=hallmark, y=checkv_length,
                   fill=keep_score_high_precision,
                   color=keep_score_high_precision,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="protist"], aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_negative, aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_negative, aes(x=hallmark, y=checkv_length,
                   fill=keep_score_high_precision,
                   color=keep_score_high_precision,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()
```




```{r}
table(viruses$hallmark[viruses$confusion_matrix_high_precision=="false positive"]>0)

table(viruses$percent_host[viruses$confusion_matrix_high_precision=="false positive"]<50)
```

## high MCC example
```{r}
viruses$keep_score_high_MCC <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_kaiju = T,
                                              include_tuning = T,
                                              include_virsorter = T)
```


```{r}
viruses$confusion_matrix_high_MCC <- "true negative"
viruses$confusion_matrix_high_MCC[viruses$seqtype=="virus" & viruses$keep_score_high_MCC<1] <- "false negative"
viruses$confusion_matrix_high_MCC[viruses$seqtype=="virus" & viruses$keep_score_high_MCC>=1] <- "true positive"
viruses$confusion_matrix_high_MCC[viruses$seqtype!="virus" & viruses$keep_score_high_MCC>=1] <- "false positive"
```

visualizing confusion matrix by taxa
```{r}
confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_MCC, viruses$seqtype, viruses$Index))
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
```



```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
```

```{r}
ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
```

```{r}
ggplot(viruses, aes(x=checkv_length, y=keep_score_high_MCC,
                   fill=confusion_matrix_high_MCC,
                   color=confusion_matrix_high_MCC)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Sequence Length (bp)") +
  ylab("Pipeline Viral Score") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()
```



## high recall example
```{r}
viruses$keep_score_high_recall <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_kaiju = T,
                                              include_tuning = T,
                                              include_virsorter = T)
```


```{r}
viruses$confusion_matrix_high_recall <- "true negative"
viruses$confusion_matrix_high_recall[viruses$seqtype=="virus" & viruses$keep_score_high_recall<1] <- "false negative"
viruses$confusion_matrix_high_recall[viruses$seqtype=="virus" & viruses$keep_score_high_recall>=1] <- "true positive"
viruses$confusion_matrix_high_recall[viruses$seqtype!="virus" & viruses$keep_score_high_recall>=1] <- "false positive"
```

accuracy:
```{r}
length(grep("true", viruses$confusion_matrix_high_recall))/nrow(viruses)
```



visualizing confusion matrix by taxa
```{r}
confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_recall, viruses$seqtype, viruses$Index))
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
```



```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
```

```{r}
p2 <- ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
p2
```

```{r}
ggplot(viruses, aes(x=checkv_completeness, y=hallmark,
                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("CheckV Completeness") +
  ylab("Number of Hallmark Genes") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()
```

```{r}
ggplot(viruses, aes(x=checkv_completeness, y=keep_score_high_MCC,
                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("CheckV Completeness") +
  ylab("Pipeline Viral Score") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()
```

```{r}
ggplot(viruses, aes(x=confusion_matrix_high_recall, y=checkv_length,
                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_boxplot() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Sequence Length (bp)") +
  ylab("Pipeline Viral Score") +
  scale_y_log10()
```



looking at false negatives
```{r}
viruses_false_negs <- viruses[(viruses$seqtype=="virus" & viruses$keep_score_high_recall<1),]
```

looking at protists calling viral
```{r}
viruses_false_pos_protists <- viruses[(viruses$seqtype=="protist" & viruses$keep_score_high_recall>=1),]
```



# Visualizing confusion matrix by number of tools


```{r}
viruses$keep_score_visualize <- viruses$keep_score
viruses$keep_score_visualize[viruses$keep_score>1] <- ">1"
viruses$keep_score_visualize <- factor(viruses$keep_score_visualize, 
                                       levels=c("-0.5", "-1", "0", "0.5","1", ">1"))
viruses$keep_score_visualize <- factor(viruses$keep_score_visualize, 
                                       labels=c("≤ 0", "≤ 0", "≤ 0", "0.5","1", "> 1"))
```

```{r}
levels(factor(viruses$keep_score_visualize))
```


```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 20", type="regular")
p1 <- ggplot(viruses, aes(x=as.factor(Index),
                   fill=keep_score_visualize, color=keep_score_visualize)) +
  geom_bar(stat="count", position="stack") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16)
  ) +
  scale_color_manual(name = 'Viral Score',
                     values = alpha(c(pal(4)), 1)) +
  scale_fill_manual(name = 'Viral Score',
                     values = alpha(c(pal(4)), 0.5)) +
  xlab("Index") +
  ylab("Sequence Count") +
  facet_wrap(~confusion_matrix, scales = "free")
p1
```


# ROC 
```{r}
library(pROC)
```

```{r}
viruses$truepositive <- rep(0, nrow(viruses))
viruses$truepositive[viruses$seqtype=="virus"] <- 1
```


```{r}
rocobj <- roc(viruses$truepositive, viruses$keep_score)
rocobj_all <- roc(viruses$truepositive, viruses$keep_score_all)
auc <- round(auc(viruses$truepositive, viruses$keep_score),4)
auc_all <- round(auc(viruses$truepositive, viruses$keep_score_all),4)
#create ROC plot
ggroc(rocobj, colour = 'steelblue', size = 2) +
  ggtitle(paste0('ROC Curve ', '(AUC = ', auc, ')')) +
  coord_equal()
ggroc(rocobj_all, colour = 'green', size = 2) +
  ggtitle(paste0('ROC Curve ', '(AUC = ', auc_all, ')'))
```
Sensitivity: The probability that the model predicts a positive outcome for an observation when indeed the outcome is positive.
Specificity: The probability that the model predicts a negative outcome for an observation when indeed the outcome is negative.




# Comparing behavior of all testing sets combined (clustering analyses)

```{r}
viral_scores <- matrix(data=0, nrow=nrow(viruses), ncol=nrow(combos_list))
num_viruses <- data.frame(toolcombo=rep(0, nrow(combos_list)),
                          num_viruses=rep(0, nrow(combos_list)))

for (i in 1:nrow(combos_list)) {
  viral_scores[,i] <- getting_viral_set_1(viruses, include_vibrant = combos_list$VIBRANT[i],
                                            include_virsorter = combos_list$VS[i],
                                            include_virsorter2 = combos_list$VS2[i],
                                            include_tuning = combos_list$CheckV[i],
                                            include_kaiju = combos_list$Kaiju[i],
                                            include_deepvirfinder = combos_list$DVF[i])
  
  num_viruses$num_viruses[i] <- table(viral_scores[,i]>=1)[[2]]
  
  num_viruses$toolcombo[i] <- combos_list$toolcombo[i]
  
  num_viruses$toolcombo2[i] <- combos_list$toolcombo2[i]
}

num_viruses$numtools <- str_count(num_viruses$toolcombo, "1")
num_viruses <- num_viruses[order(num_viruses$num_viruses, decreasing=F),]
num_viruses$toolcombo <- factor(num_viruses$toolcombo, levels = unique(num_viruses$toolcombo))
num_viruses$toolcombo2 <- factor(num_viruses$toolcombo2, levels = unique(num_viruses$toolcombo2))
num_viruses$numtools <- as.factor(num_viruses$numtools)
```


```{r}
ggplot(num_viruses, aes(x=toolcombo, y=num_viruses, 
                                  color=numtools, fill=numtools)) +
  geom_point() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Num Viruses Predicted")

ggplot(num_viruses, aes(x=toolcombo2, y=num_viruses, 
                                  color=numtools, fill=numtools)) +
  geom_point() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Num Viruses Predicted")
```

```{r}
ggplot(num_viruses, aes(x=numtools, y=num_viruses)) +
  geom_boxplot(aes(color=numtools)) +
  geom_point(aes(color=numtools, fill=numtools)) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Tools") +
  ylab("Num Viruses Predicted")
```


```{r}
viral_scores_nozeros <- viral_scores[rowSums(viral_scores)>0,]
viral_scores_nozeros <- viral_scores_nozeros + 1
viral_scores_nozeros <- as.data.frame(viral_scores_nozeros)

colnames(viral_scores_nozeros) <- num_viruses$toolcombo2
```

```{r}
library(phyloseq)
```


```{r}
tooldata <- num_viruses

rownames(tooldata) <- tooldata$toolcombo2
```

```{r}
physeq_pooled <- phyloseq(otu_table(viral_scores_nozeros, taxa_are_rows = T),
                                     sample_data(tooldata))
```

```{r}
ordination <- phyloseq::ordinate(physeq =physeq_pooled, method = "PCoA", distance = "bray")
phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numtools", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw() +
  geom_label(label=tooldata$toolcombo)

phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numtools", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw()
```
to do: try coloring above based on the F1 scores of the testing set on each combination

```{r}
bray_dist <- phyloseq::distance(physeq_pooled, method="bray")
clusters <- hclust(dist(bray_dist))
plot(clusters)

myclusters <- cutree(clusters, h=1.1)
```


```{r}
names(myclusters[myclusters==1])
names(myclusters[myclusters==2])
names(myclusters[myclusters==3])
names(myclusters[myclusters==4])
names(myclusters[myclusters==5])

myclusters_df <- tibble(combo=names(myclusters),
                            cluster_index=myclusters)

myclusters_df <- separate(myclusters_df, col=combo, into=c("CheckV", "DVF",
                                                            "Kaiju", "VIBRANT",
                                                            "VirSorter", "VirSorter2"),
                          sep=" ", remove = F)


tool_count <- as.data.frame(rbind(table(myclusters_df$CheckV, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$DVF, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$Kaiju, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VIBRANT, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VirSorter, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VirSorter2, myclusters_df$cluster_index)[2,])
                    )

tool_count$method <- c("CheckV", "DVF", "Kaiju", "VIBRANT", "VirSorter", "VirSorter2")

tool_count <- melt(tool_count)

colnames(tool_count) <- c("tool", "cluster_index", "tool_count")
```

```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")

ggplot(tool_count, aes(x=cluster_index, y=tool_count,
                   fill=cluster_index,
                   color=cluster_index)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  xlab("Cluster") +
  ylab("Number of Times in Cluster") + 
  facet_wrap(~tool, scales = "free")
```


